Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach

Yu Zheng, Bowei Chen, Timothy Hospedales, Yongxin Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which contains some constituents of the index. The tracking error optimisation is quadratic and NP-hard when taking the constraint l0 into account so it is usually solved by heuristic methods such as evolutionary algorithms. This paper introduces a simple, efficient and scalable connectionist model as an alternative. We propose a novel reparametrisation method and then solve the optimisation problem with stochastic neural networks. The proposed approach is examined with S&P 500 index data for more than 10 years and compared with widely used index tracking approaches such as forward and backward selection and the largest market capitalisation methods. The empirical results show our model achieves excellent performance. Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes. Meanwhile it offers comparable performance to the others on secondary criteria such as volatility, Sharpe ratio and maximum drawdown.
Original languageEnglish
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020)
PublisherAssociation for the Advancement of Artificial Intelligence AAAI
Pages1242-1249
Number of pages8
ISBN (Print)978-1-57735-835-0
DOIs
Publication statusPublished - 3 Apr 2020
Event34th AAAI Conference on Artificial Intelligence - New York, United States
Duration: 7 Feb 202012 Feb 2020
Conference number: 34
https://aaai.org/Conferences/AAAI-19/

Publication series

Name
PublisherAAAI
Number1-10
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference34th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2020
Country/TerritoryUnited States
CityNew York
Period7/02/2012/02/20
Internet address

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